UltraSound3D:Main: Difference between revisions
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==References== | |||
* [https://www.sintef.no/projectweb/usigt-en/courses/ultrasound-in-neurosurgery/ https://www.sintef.no/projectweb/usigt-en/courses/ultrasound-in-neurosurgery/] | |||
* [https://link.springer.com/chapter/10.1007/978-3-642-40811-3_5 Global Registration of Ultrasound to MRI Using the LC<sup>2</sup> Metric for Enabling Neurosurgical Guidance ] [https://odu.box.com/s/7m50wfga8wd39ez8vwxjiuzhqe1xei37 Alternative Link] | |||
:'''Abstract''' : Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC2)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters. | |||
Latest revision as of 02:35, 25 April 2017
References
- https://www.sintef.no/projectweb/usigt-en/courses/ultrasound-in-neurosurgery/
- Global Registration of Ultrasound to MRI Using the LC2 Metric for Enabling Neurosurgical Guidance Alternative Link
- Abstract : Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC2)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters.